{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "    \n",
    "chipo = pd.read_csv(url, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z6vR/LU/FN3v27Oce9/X10dfXtyYxRkRMOP39/fT39zd1bkensJZ0KvAb4F1U7QjLJE0D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\nrDbS1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b53fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the Series of the names\n",
    "x = chipo.item_name\n",
    "\n",
    "# use the Counter class from collections to create a dictionary with keys(text) and frequency\n",
    "letter_counts = Counter(x)\n",
    "\n",
    "# convert the dictionary to a DataFrame\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "\n",
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = df[0].sort_values(ascending = True)[45:50]\n",
    "\n",
    "# create the plot\n",
    "df.plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 40.0)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Swy/CqBdGMXzkcLpO7uqdtxj4BLmvAmK5qTN0oGfohwnjJ3DxNy5m57SdYZ0z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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b80acd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a list of prices\n",
    "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space\n",
    "\n",
    "# then groupby the orders and sum\n",
    "orders = chipo.groupby('order_id').sum()\n",
    "\n",
    "# creates the scatterplot\n",
    "# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')\n",
    "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Order Price')\n",
    "plt.ylabel('Items ordered')\n",
    "plt.title('Number of items ordered per order price')\n",
    "plt.ylim(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
